Agente de investigación profunda de IA autohospedado usando n8n, Apify y OpenAI o3
Avanzado
Este es unAIflujo de automatización del dominio deautomatización que contiene 87 nodos.Utiliza principalmente nodos como If, Set, Code, Form, Merge, combinando tecnología de inteligencia artificial para lograr automatización inteligente. Agente de investigación profunda de IA autohospedado usando n8n, Apify y OpenAI o3
Requisitos previos
- •Clave de API de Notion
- •Pueden requerirse credenciales de autenticación para la API de destino
- •Clave de API de OpenAI
- •Clave de API de Google Gemini
Nodos utilizados (87)
Categoría
Vista previa del flujo de trabajo
Visualización de las conexiones entre nodos, con soporte para zoom y panorámica
Exportar flujo de trabajo
Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo
{
"meta": {
"instanceId": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9",
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "a342005e-a88e-419b-b929-56ecbba4a936",
"name": "Analizador de salida estructurada",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
1300,
1180
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"learnings\": {\n \"type\": \"array\",\n \"description\": \"List of learnings, max of 3.\",\n \"items\": { \"type\": \"string\" }\n },\n \"followUpQuestions\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\",\n \"description\": \"List of follow-up questions to research the topic further, max of 3.\"\n }\n }\n }\n}"
},
"typeVersion": 1.2
},
{
"id": "126b8151-6d20-43b8-8028-8163112c4c5b",
"name": "Establecer variables",
"type": "n8n-nodes-base.set",
"position": [
-1360,
-460
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "df28b12e-7c20-4ff5-b5b8-dc773aa14d4b",
"name": "request_id",
"type": "string",
"value": "={{ $execution.id }}"
},
{
"id": "9362c1e7-717d-444a-8ea2-6b5f958c9f3f",
"name": "prompt",
"type": "string",
"value": "={{ $json['What would you like to research?'] }}"
},
{
"id": "09094be4-7844-4a9e-af82-cc8e39322398",
"name": "depth",
"type": "number",
"value": "={{\n!isNaN($json['input-depth'][0].toNumber())\n ? $json['input-depth'][0].toNumber()\n : 1\n}}"
},
{
"id": "3fc30a30-7806-4013-835d-97e27ddd7ae1",
"name": "breadth",
"type": "number",
"value": "={{\n!isNaN($json['input-breadth'][0].toNumber())\n ? $json['input-breadth'][0].toNumber()\n : 1\n}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "1d0fb87b-263d-46c2-b016-a29ba1d407ab",
"name": "Modelo de chat OpenAI",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1120,
1180
],
"parameters": {
"model": {
"__rl": true,
"mode": "id",
"value": "o3-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "39b300d9-11ba-44f6-8f43-2fe256fe4856",
"name": "Modelo de chat OpenAI1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-860,
1760
],
"parameters": {
"model": {
"__rl": true,
"mode": "id",
"value": "o3-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "018da029-a796-45c5-947c-791e087fe934",
"name": "Modelo de chat OpenAI2",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-1060,
-300
],
"parameters": {
"model": {
"__rl": true,
"mode": "id",
"value": "o3-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "525da936-a9eb-4523-b27a-ff6ae7b0e5ef",
"name": "Analizador de salida estructurada1",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-840,
-300
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"questions\": {\n \"type\": \"array\",\n \"description\": \"Follow up questions to clarify the research direction, max of 3.\",\n \"items\": {\n \"type\": \"string\"\n }\n }\n }\n}"
},
"typeVersion": 1.2
},
{
"id": "e6664883-cff4-4e09-881e-6b6f684f9cac",
"name": "Al enviar formulario",
"type": "n8n-nodes-base.formTrigger",
"position": [
-1760,
-460
],
"webhookId": "026629c8-7644-493c-b830-d9c72eea307d",
"parameters": {
"options": {
"path": "deep_research",
"ignoreBots": true,
"buttonLabel": "Next"
},
"formTitle": " DeepResearcher",
"formFields": {
"values": [
{
"fieldType": "html"
}
]
},
"formDescription": "=DeepResearcher is a multi-step, recursive approach using the internet to solve complex research tasks, accomplishing in tens of minutes what a human would take many hours.\n\nTo use, provide a short summary of what the research and how \"deep\" you'd like the workflow to investigate. Note, the higher the numbers the more time and cost will occur for the research.\n\nThe workflow is designed to complete independently and when finished, a report will be saved in a designated Notion Database."
},
"typeVersion": 2.2
},
{
"id": "6b8ebc08-c0b1-4af8-99cc-79d09eea7316",
"name": "Generar consultas SERP",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-1040,
820
],
"parameters": {
"text": "=Given the following prompt from the user, generate a list of SERP queries to research the topic.\nReduce the number of words in each query to its keywords only.\nReturn a maximum of {{ $('JobType Router').first().json.data.breadth }} queries, but feel free to return less if the original prompt is clear. Make sure each query is unique and not similar to each other: <prompt>{{ $('JobType Router').first().json.data.query.trim() }}</prompt>\n\n{{\n$('JobType Router').first().json.data.learnings.length\n ? `Here are some learnings from previous research, use them to generate more specific queries:\\n${$('JobType Router').first().json.data.learnings.map(text => `* ${text}`).join('\\n')}`\n : ''\n}}",
"messages": {
"messageValues": [
{
"type": "HumanMessagePromptTemplate",
"message": "=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n - Be highly organized.\n - Suggest solutions that I didn't think about.\n - Be proactive and anticipate my needs.\n - Treat me as an expert in all subject matter.\n - Mistakes erode my trust, so be accurate and thorough.\n - Provide detailed explanations, I'm comfortable with lots of detail.\n - Value good arguments over authorities, the source is irrelevant.\n - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n - You may use high levels of speculation or prediction, just flag it for me."
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.5
},
{
"id": "34e1fa5d-bc0c-4b9e-84a7-35db2b08c772",
"name": "Analizador de salida estructurada2",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-860,
980
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"queries\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"query\": {\n \"type\": \"string\",\n \"description\": \"The SERP query\"\n },\n \"researchGoal\": {\n \"type\": \"string\",\n \"description\": \"First talk about the goal of the research that this query is meant to accomplish, then go deeper into how to advance the research once the results are found, mention additional research directions. Be as specific as possible, especially for additional research directions.\"\n }\n }\n }\n }\n }\n}"
},
"typeVersion": 1.2
},
{
"id": "be6dd6a2-aacf-4682-8f13-8ae24c4249a3",
"name": "Modelo de chat OpenAI3",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-1040,
980
],
"parameters": {
"model": {
"__rl": true,
"mode": "id",
"value": "o3-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "d5ce6e21-cd07-44fa-b6d0-90bf7531ee01",
"name": "Establecer consulta inicial",
"type": "n8n-nodes-base.set",
"position": [
-580,
180
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "acb41e93-70c6-41a3-be0f-e5a74ec3ec88",
"name": "query",
"type": "string",
"value": "={{ $('JobType Router').first().json.data.query }}"
},
{
"id": "7fc54063-b610-42bc-a250-b1e8847c4d1e",
"name": "learnings",
"type": "array",
"value": "={{ $('JobType Router').first().json.data.learnings }}"
},
{
"id": "e8f1c158-56fb-41c8-8d86-96add16289bb",
"name": "breadth",
"type": "number",
"value": "={{ $('JobType Router').first().json.data.breadth }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "9de6e4a1-a2b5-4a6f-948e-a0585edcae48",
"name": "SERP a elementos",
"type": "n8n-nodes-base.splitOut",
"position": [
-700,
820
],
"parameters": {
"options": {},
"fieldToSplitOut": "output.queries"
},
"typeVersion": 1
},
{
"id": "2c9c4cdf-942b-494c-83fb-ed5ec37385ee",
"name": "Referencia de elemento",
"type": "n8n-nodes-base.noOp",
"position": [
-220,
1020
],
"parameters": {},
"typeVersion": 1
},
{
"id": "703c57af-de19-4f00-b580-711a272fa5ca",
"name": "Objetivo de investigación + Hallazgos",
"type": "n8n-nodes-base.set",
"position": [
1460,
1160
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9acec2cc-64c8-4e62-bed4-c3d9ffab1379",
"name": "researchGoal",
"type": "string",
"value": "={{ $('Item Ref').first().json.researchGoal }}"
},
{
"id": "1b2d2dad-429b-4fc9-96c5-498f572a85c3",
"name": "learnings",
"type": "array",
"value": "={{ $json.output.learnings }}"
},
{
"id": "7025f533-02ab-4031-9413-43390fb61f05",
"name": "followUpQuestions",
"type": "string",
"value": "={{ $json.output.followUpQuestions }}"
},
{
"id": "c9e34ea4-5606-46d6-8d66-cb42d772a8b4",
"name": "urls",
"type": "array",
"value": "={{\n$('Get Markdown + URL')\n .all()\n .map(item => item.json.url)\n}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "16ed2835-3af4-45e3-b5a7-e4342d571aa0",
"name": "Acumular resultados",
"type": "n8n-nodes-base.set",
"position": [
-200,
180
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "db509e90-9a86-431f-8149-4094d22666cc",
"name": "should_stop",
"type": "boolean",
"value": "={{\n$runIndex >= ($('JobType Router').first().json.data.depth)\n}}"
},
{
"id": "90986e2b-8aca-4a22-a9db-ed8809d6284d",
"name": "all_learnings",
"type": "array",
"value": "={{\nArray($runIndex+1)\n .fill(0)\n .flatMap((_,idx) => {\n try {\n return $('Generate Learnings')\n .all(0,idx)\n .flatMap(item => item.json.data.flatMap(d => d.learnings))\n } catch (e) {\n return []\n }\n })\n}}"
},
{
"id": "3eade958-e8ab-4975-aac4-f4a4a983c163",
"name": "all_urls",
"type": "array",
"value": "={{\nArray($runIndex+1)\n .fill(0)\n .flatMap((_,idx) => {\n try {\n return $('Generate Learnings')\n .all(0,idx)\n .flatMap(item => item.json.data.flatMap(d => d.urls))\n } catch (e) {\n return []\n }\n })\n}}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "0011773e-85c6-4fe1-8554-c23ce50706d0",
"name": "Resultados de investigación profunda",
"type": "n8n-nodes-base.set",
"position": [
160,
360
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={{ $('Generate Learnings').item.json }}"
},
"typeVersion": 3.4
},
{
"id": "c0b646d0-1246-4864-8f79-8b7a66e4e083",
"name": "Resultados a elementos",
"type": "n8n-nodes-base.splitOut",
"position": [
320,
360
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1
},
{
"id": "3c52ec3e-c952-4b5f-ab12-f1b5d02aba74",
"name": "Establecer siguientes consultas",
"type": "n8n-nodes-base.set",
"position": [
480,
360
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "d88bfe95-9e73-4d25-b45c-9f164b940b0e",
"name": "query",
"type": "string",
"value": "=Previous research goal: {{ $json.researchGoal }}\nFollow-up research directions: {{ $json.followUpQuestions.map(q => `\\n${q}`).join('') }}"
},
{
"id": "4aa20690-d998-458a-b1e4-0d72e6a68e6b",
"name": "learnings",
"type": "array",
"value": "={{ $('Accumulate Results').item.json.all_learnings }}"
},
{
"id": "89acafae-b04a-4d5d-b08b-656e715654e4",
"name": "breadth",
"type": "number",
"value": "={{ $('JobType Router').first().json.data.breadth }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "bc59dddc-2b03-481f-91c6-ea8aa378eef0",
"name": "Por cada consulta...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-420,
860
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "903c31c4-5fdc-4cb6-8baa-402555997266",
"name": "Retroalimentación a elementos",
"type": "n8n-nodes-base.splitOut",
"position": [
-720,
-460
],
"parameters": {
"options": {},
"fieldToSplitOut": "output.questions"
},
"typeVersion": 1
},
{
"id": "59ff671d-5d4f-42ff-b94f-ed30a8531e55",
"name": "Hacer preguntas de clarificación",
"type": "n8n-nodes-base.form",
"position": [
-360,
-380
],
"webhookId": "d3375ba6-0008-4fcb-96bc-110374de2603",
"parameters": {
"options": {
"formTitle": "DeepResearcher",
"buttonLabel": "Answer",
"formDescription": "=<img\n src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw\"\n width=\"100%\"\n style=\"border:1px solid #ccc\"\n/>\n<p style=\"text-align:left\">\nAnswer the following clarification questions to assist the DeepResearcher better under the research topic.\n</p>\n<hr style=\"display:block;margin-top:16px;margin-bottom:0\" />\n<p style=\"text-align:left;font-family:sans-serif;font-weight:700;\">\nTotal {{ $('Feedback to Items').all().length }} questions.\n</p>"
},
"formFields": {
"values": [
{
"fieldType": "textarea",
"fieldLabel": "={{ $json[\"output.questions\"] }}",
"placeholder": "=",
"requiredField": true
}
]
}
},
"typeVersion": 1
},
{
"id": "1c2cf79b-f1a1-4ecc-bb45-3d4460c947bd",
"name": "Por cada pregunta...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-540,
-460
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "0c9ffa99-2687-4df5-8581-0c5b0b2657a9",
"name": "Subflujo de investigación profunda",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-1880,
820
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "requestId",
"type": "any"
},
{
"name": "jobType"
},
{
"name": "data",
"type": "object"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "127ab95d-bf89-4762-bfb5-34521e620ae2",
"name": "Nota adhesiva",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1140,
-680
],
"parameters": {
"color": 7,
"width": 1000,
"height": 560,
"content": "## 2. Ask Clarifying Questions\n[Read more about form nodes](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.form/)\n\nTo handle the clarification questions generated by the LLM, I used the same technique found in my \"AI Interviewer\" template ([link](https://n8n.io/workflows/2566-conversational-interviews-with-ai-agents-and-n8n-forms/)).\nThis involves a looping of dynamically generated forms to collect answers from the user."
},
"typeVersion": 1
},
{
"id": "e87c0f19-6002-4aa2-931a-ca7546146a84",
"name": "Preguntas de clarificación",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-1040,
-460
],
"parameters": {
"text": "=Given the following query from the user, ask some follow up questions to clarify the research direction. Return a maximum of 3 questions, but feel free to return less if the original query is clear: <query>{{ $json.prompt }}</query>`",
"messages": {
"messageValues": [
{
"type": "HumanMessagePromptTemplate",
"message": "=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n - Be highly organized.\n - Suggest solutions that I didn't think about.\n - Be proactive and anticipate my needs.\n - Treat me as an expert in all subject matter.\n - Mistakes erode my trust, so be accurate and thorough.\n - Provide detailed explanations, I'm comfortable with lots of detail.\n - Value good arguments over authorities, the source is irrelevant.\n - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n - You may use high levels of speculation or prediction, just flag it for me."
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.5
},
{
"id": "b84f9c4a-c1de-4288-bab2-b7f5ffb8b542",
"name": "Nota adhesiva1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-660,
-60
],
"parameters": {
"color": 7,
"width": 1360,
"height": 640,
"content": "## 6. Perform DeepSearch Loop\n[Learn more about the Looping in n8n](https://docs.n8n.io/flow-logic/looping/#creating-loops)\n\nThe key of the Deep Research flow is its extensive data collection capability. In this implementation, this capability is represented by a recursive web search & scrape loop which starts with the original query and extended by AI-generated subqueries. How many subqueries to generate are determined the depth and breadth parameters specified.\n\n\"Learnings\" are generated for each subquery and accumulate on each iteration of the loop. When the loop finishes when depth limit is reached, all learnings are collected and it's these learnings are what we use to generate the report."
},
"typeVersion": 1
},
{
"id": "0a8c3a01-d4d4-4075-9521-035b7df9aa5a",
"name": "Formulario final",
"type": "n8n-nodes-base.form",
"position": [
960,
-420
],
"webhookId": "88f2534b-2b82-4b40-a4bc-97d96384e8fd",
"parameters": {
"options": {},
"operation": "completion",
"completionTitle": "=Thank you for using DeepResearcher.",
"completionMessage": "=You may now close this window."
},
"typeVersion": 1
},
{
"id": "44a3603f-a5a1-4031-8c5f-c748b1007b47",
"name": "Iniciar investigación profunda",
"type": "n8n-nodes-base.executeWorkflow",
"position": [
600,
-420
],
"parameters": {
"mode": "each",
"options": {
"waitForSubWorkflow": false
},
"workflowId": {
"__rl": true,
"mode": "id",
"value": "={{ $workflow.id }}"
},
"workflowInputs": {
"value": {
"data": "={{\n{\n \"query\": $('Get Initial Query').first().json.query,\n \"learnings\": [],\n \"depth\": $('Set Variables').first().json.depth,\n \"breadth\": $('Set Variables').first().json.breadth,\n}\n}}",
"jobType": "deepresearch_initiate",
"requestId": "={{ $('Set Variables').first().json.request_id }}"
},
"schema": [
{
"id": "requestId",
"display": true,
"removed": false,
"required": false,
"displayName": "requestId",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "jobType",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "jobType",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "data",
"type": "object",
"display": true,
"removed": false,
"required": false,
"displayName": "data",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": true
}
},
"typeVersion": 1.2
},
{
"id": "b243eb76-9ed9-4327-968f-c21844bc9df4",
"name": "Datos de ejecución",
"type": "n8n-nodes-base.executionData",
"position": [
-1700,
820
],
"parameters": {
"dataToSave": {
"values": [
{
"key": "requestId",
"value": "={{ $json.requestId }}"
},
{
"key": "=jobType",
"value": "={{ $json.jobType }}"
}
]
}
},
"typeVersion": 1
},
{
"id": "57ca4b22-9349-4b34-8f6b-c502905b5172",
"name": "Enrutador por tipo de trabajo",
"type": "n8n-nodes-base.switch",
"position": [
-1520,
820
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "initiate",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.jobType }}",
"rightValue": "deepresearch_initiate"
}
]
},
"renameOutput": true
},
{
"outputKey": "learnings",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "ecbfa54d-fc97-48c5-8d3d-f0538b8d727b",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.jobType }}",
"rightValue": "deepresearch_learnings"
}
]
},
"renameOutput": true
},
{
"outputKey": "report",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "392f9a98-ec22-4e57-9c8e-0e1ed6b7dafa",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.jobType }}",
"rightValue": "deepresearch_report"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "1f880fbd-71ba-4e5b-8d99-9654ae0c949f",
"name": "Modelo de chat OpenAI4",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-20,
-280
],
"parameters": {
"model": {
"__rl": true,
"mode": "id",
"value": "o3-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "ea65589b-106f-4ff1-a6f2-763393c2cb07",
"name": "Obtener consulta inicial",
"type": "n8n-nodes-base.set",
"position": [
-360,
-540
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "14b77741-c3c3-4bd2-be6e-37bd09fcea2b",
"name": "query",
"type": "string",
"value": "=Initial query: {{ $('Set Variables').first().json.prompt }}\nFollow-up Questions and Answers:\n{{\n$input.all()\n .map(item => {\n const q = Object.keys(item.json)[0];\n const a = item.json[q];\n return `question: ${q}\\nanswer: ${a}`;\n })\n .join('\\n')\n}}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "09a363f2-6300-430d-8c7e-3e1611ab8e68",
"name": "Analizador de salida estructurada4",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
160,
-280
],
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n \"description\":\" A short title summarising the research topic\"\n },\n \"description\": {\n \"type\": \"string\",\n \"description\": \"A short description to summarise the research topic\"\n }\n }\n}"
},
"typeVersion": 1.2
},
{
"id": "9910804e-8376-4e2e-a011-7d32ca951edf",
"name": "Crear fila",
"type": "n8n-nodes-base.notion",
"position": [
300,
-420
],
"parameters": {
"title": "={{ $json.output.title }}",
"options": {},
"resource": "databasePage",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "19486dd6-0c0c-80da-9cb7-eb1468ea9afd",
"cachedResultUrl": "https://www.notion.so/19486dd60c0c80da9cb7eb1468ea9afd",
"cachedResultName": "n8n DeepResearch"
},
"propertiesUi": {
"propertyValues": [
{
"key": "Description|rich_text",
"textContent": "={{ $json.output.description }}"
},
{
"key": "Status|status",
"statusValue": "Not started"
},
{
"key": "Request ID|rich_text",
"textContent": "={{ $('Set Variables').first().json.request_id }}"
},
{
"key": "Name|title",
"title": "={{ $json.output.title }}"
}
]
}
},
"credentials": {
"notionApi": {
"id": "iHBHe7ypzz4mZExM",
"name": "Notion account"
}
},
"typeVersion": 2.2
},
{
"id": "9f06d9ae-220d-4f5b-bcbf-761b88ba255c",
"name": "Generador de página de informe",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-20,
-420
],
"parameters": {
"text": "=Create a suitable title for the research report which will be created from the user's query.\n<query>{{ $json.query }}</query>",
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.5
},
{
"id": "5b434bdc-e1e7-4348-b03d-dcbb6a485263",
"name": "Nota adhesiva2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-120,
-680
],
"parameters": {
"color": 7,
"width": 600,
"height": 560,
"content": "## 3. Create Empty Report Page in Notion\n[Read more about the Notion node](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.notion/)\n\nSome thought was given where to upload the final report and Notion was selected due to familiarity. This can be easily changed to whatever wiki tools you prefer.\n\nIf you're following along however, here's the Notion database you need to replicate - [Jim's n8n DeepResearcher Database](https://jimleuk.notion.site/19486dd60c0c80da9cb7eb1468ea9afd?v=19486dd60c0c805c8e0c000ce8c87acf)."
},
"typeVersion": 1
},
{
"id": "0cfb3548-14a8-4dcc-8362-a7ca1d4c328f",
"name": "Nota adhesiva3",
"type": "n8n-nodes-base.stickyNote",
"position": [
500,
-680
],
"parameters": {
"color": 7,
"width": 640,
"height": 560,
"content": "## 4. Trigger DeepResearch Asynchronously\n[Learn more about the Execute Trigger node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflow/)\n\nn8n handles asynchronous jobs by spinning them off as separate executions. This basically means the user doesn't have to wait or keep their browser window open for our researcher to do its job.\n\nOnce we initiate the Deepresearcher job, we can close out the onboarding journey for a nice user experience."
},
"typeVersion": 1
},
{
"id": "b90456d0-fae3-4809-bc13-55649e6e919a",
"name": "Nota adhesiva4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1160,
620
],
"parameters": {
"color": 7,
"width": 620,
"height": 540,
"content": "## 7. Generate Search Queries\n[Learn more about the Basic LLM node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/)\n\nMuch like a human researcher, the DeepResearcher will rely on web search and content as the preferred source of information. To ensure it can cover a wide range of sources, the AI can first generate relevant research queries of which each can be explored separately."
},
"typeVersion": 1
},
{
"id": "9fd00d55-1c76-425b-8386-7bc5b2bb47ac",
"name": "¿Se alcanzó la profundidad?",
"type": "n8n-nodes-base.if",
"position": [
-40,
180
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "75d18d88-6ba6-43df-bef7-3e8ad99ad8bd",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json.should_stop }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "f658537b-4f4c-4427-a66f-56cfd950bffc",
"name": "Obtener resultados de investigación",
"type": "n8n-nodes-base.set",
"position": [
160,
180
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "90b3da00-dcd5-4289-bd45-953146a3b0ba",
"name": "all_learnings",
"type": "array",
"value": "={{ $json.all_learnings }}"
},
{
"id": "623dbb3d-83a1-44a9-8ad3-48d92bc42811",
"name": "all_urls",
"type": "array",
"value": "={{ $json.all_urls }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "6059f3ba-e4a0-4528-894c-6080eedb91c3",
"name": "Obtener fila existente",
"type": "n8n-nodes-base.notion",
"position": [
-1040,
180
],
"parameters": {
"limit": 1,
"filters": {
"conditions": [
{
"key": "Request ID|rich_text",
"condition": "equals",
"richTextValue": "={{ $json.requestId.toString() }}"
}
]
},
"options": {},
"resource": "databasePage",
"matchType": "allFilters",
"operation": "getAll",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "19486dd6-0c0c-80da-9cb7-eb1468ea9afd",
"cachedResultUrl": "https://www.notion.so/19486dd60c0c80da9cb7eb1468ea9afd",
"cachedResultName": "n8n DeepResearch"
},
"filterType": "manual"
},
"credentials": {
"notionApi": {
"id": "iHBHe7ypzz4mZExM",
"name": "Notion account"
}
},
"typeVersion": 2.2
},
{
"id": "100625bb-bf9a-4993-b387-1c61e486ba6d",
"name": "Establecer en progreso",
"type": "n8n-nodes-base.notion",
"position": [
-840,
180
],
"parameters": {
"pageId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {},
"resource": "databasePage",
"operation": "update",
"propertiesUi": {
"propertyValues": [
{
"key": "Status|status",
"statusValue": "In progress"
}
]
}
},
"credentials": {
"notionApi": {
"id": "iHBHe7ypzz4mZExM",
"name": "Notion account"
}
},
"typeVersion": 2.2
},
{
"id": "864332ea-dd25-4347-a49d-68ed6495c1a9",
"name": "Establecer como finalizado",
"type": "n8n-nodes-base.notion",
"position": [
1680,
1600
],
"parameters": {
"pageId": {
"__rl": true,
"mode": "id",
"value": "={{ $('Get Existing Row1').first().json.id }}"
},
"options": {},
"resource": "databasePage",
"operation": "update",
"propertiesUi": {
"propertyValues": [
{
"key": "Status|status",
"statusValue": "Done"
},
{
"key": "Last Updated|date",
"date": "={{ $now.toISO() }}"
}
]
}
},
"credentials": {
"notionApi": {
"id": "iHBHe7ypzz4mZExM",
"name": "Notion account"
}
},
"executeOnce": true,
"typeVersion": 2.2
},
{
"id": "6771568a-e6bd-4c89-a535-089fd1c18fc3",
"name": "Etiquetas a elementos",
"type": "n8n-nodes-base.splitOut",
"position": [
-60,
1600
],
"parameters": {
"options": {},
"fieldToSplitOut": "tag"
},
"typeVersion": 1
},
{
"id": "47fce580-7b5b-4bc6-ba52-a8e7af6595b5",
"name": "Convertir a HTML",
"type": "n8n-nodes-base.markdown",
"position": [
-380,
1600
],
"parameters": {
"mode": "markdownToHtml",
"options": {
"tables": true
},
"markdown": "={{ $json.text }}"
},
"typeVersion": 1
},
{
"id": "e2fb5a31-9ca5-487b-a7f8-f020759ec53a",
"name": "HTML a matriz",
"type": "n8n-nodes-base.set",
"position": [
-220,
1600
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "851b8a3f-c2d3-41ad-bf60-4e0e667f6c58",
"name": "tag",
"type": "array",
"value": "={{ $json.data.match(/<table[\\s\\S]*?<\\/table>|<ul[\\s\\S]*?<\\/ul>|<[^>]+>[^<]*<\\/[^>]+>/g) }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "5275f9dd-5420-4c59-a330-5f2775b47e51",
"name": "Generador de bloques Notion",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
100,
1600
],
"parameters": {
"text": "={{ $json.tag.trim() }}",
"messages": {
"messageValues": [
{
"message": "=Convert the following html into its equivalent Notion Block as per Notion's API schema.\n* Ensure the content is always included and remains the same.\n* Return only a json response.\n* Generate child-level blocks. Should not define \"parent\" or \"children\" property.\n* Strongly prefer headings, paragraphs, tables and lists type blocks.\n* available headings are heading_1, heading_2 and heading_3 - h4,h5,h6 should use heading_3 type instead. ensure headings use the rich text definition.\n* ensure lists blocks include all list items.\n\n## Examples\n\n1. headings\n```\n<h3 id=\"references\">References</h3>\n```\nwould convert to \n```\n{\"object\": \"block\", \"type\": \"heading_3\", \"heading_3\": { \"rich_text\": [{\"type\": \"text\",\"text\": {\"content\": \"References\"}}]}}\n```\n\n2. lists\n```\n<ul><li>hello</li><li>world</li></ul>\n```\nwould convert to\n```\n[\n{\n \"object\": \"block\",\n \"type\": \"bulleted_list_item\",\n \"bulleted_list_item\": {\"rich_text\": [{\"type\": \"text\",\"text\": {\"content\": \"hello\"}}]}\n},\n{\n \"object\": \"block\",\n \"type\": \"bulleted_list_item\",\n \"bulleted_list_item\": {\"rich_text\": [{\"type\": \"text\",\"text\": {\"content\": \"world\"}}]}\n}\n]\n```\n\n3. tables\n```\n<table>\n <thead>\n <tr><th>Technology</th><th>Potential Impact</th></tr>\n </thead>\n <tbody>\n <tr>\n <td>5G Connectivity</td><td>Enables faster data speeds and advanced apps</td>\n </tr>\n </tbody>\n</table>\n```\nwould convert to\n```\n{\n \"object\": \"block\",\n \"type\": \"table\",\n \"table\": {\n \"table_width\": 2,\n \"has_column_header\": true,\n \"has_row_header\": false,\n \"children\": [\n {\n \"object\": \"block\",\n \"type\": \"table_row\",\n \"table_row\": {\n \"cells\": [\n [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \"Technology\",\n \"link\": null\n }\n },\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \"Potential Impact\",\n \"link\": null\n }\n }\n ],\n [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \"5G Connectivity\",\n \"link\": null\n }\n },\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \"Enables faster data speeds and advanced apps\",\n \"link\": null\n }\n }\n ]\n ]\n }\n }\n ]\n }\n}\n```\n4. anchor links\nSince Notion doesn't support anchor links, just convert them to rich text blocks instead.\n```\n<a href=\"#module-0-pre-course-setup-and-learning-principles\">Module 0: Pre-Course Setup and Learning Principles</a>\n```\nconverts to\n```\n{\n \"object\": \"block\",\n \"type\": \"paragraph\",\n \"paragraph\": {\n \"rich_text\": [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \"Module 0: Pre-Course Setup and Learning Principles\"\n }\n }\n ]\n }\n}\n```\n5. Invalid html parts\nWhen the html is not syntax valid eg. orphaned closing tags, then just skip the conversion and use an empty rich text block.\n```\n</li>\\n</ol>\n```\ncan be substituted with\n```\n{\n \"object\": \"block\",\n \"type\": \"paragraph\",\n \"paragraph\": {\n \"rich_text\": [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \" \"\n }\n }\n ]\n }\n}\n```"
}
]
},
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "30e73ecf-5994-4229-b7f6-01e043e0e65b",
"name": "Modelo de chat Google Gemini",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
80,
1760
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "dSxo6ns5wn658r8N",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "85ce9f7e-0369-41bd-8c31-c4217f400472",
"name": "Analizar bloques JSON",
"type": "n8n-nodes-base.set",
"onError": "continueRegularOutput",
"position": [
420,
1600
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "73fcb8a0-2672-4bd5-86de-8075e1e02baf",
"name": "=block",
"type": "array",
"value": "={{\n(function(){\n const block = $json.text\n .replace('```json', '')\n .replace('```', '')\n .trim()\n .parseJson();\n if (Array.isArray(block)) return block;\n if (block.type.startsWith('heading_')) {\n const prev = Number(block.type.split('_')[1]);\n const next = Math.max(1, prev - 1);\n if (next !== prev) {\n block.type = `heading_${next}`;\n block[`heading_${next}`] = Object.assign({}, block[`heading_${prev}`]);\n block[`heading_${prev}`] = undefined;\n }\n }\n return [block];\n})()\n}}"
}
]
}
},
"executeOnce": false,
"typeVersion": 3.4
},
{
"id": "349f4323-d65f-4845-accc-6f51340a84c4",
"name": "Subir a página Notion",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"maxTries": 2,
"position": [
1680,
1760
],
"parameters": {
"url": "=https://api.notion.com/v1/blocks/{{ $('Get Existing Row1').first().json.id }}/children",
"method": "PATCH",
"options": {
"timeout": "={{ 1000 * 60 }}"
},
"jsonBody": "={{\n{\n \"children\": $json.block\n}\n}}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "Notion-Version",
"value": "2022-06-28"
}
]
},
"nodeCredentialType": "notionApi"
},
"credentials": {
"notionApi": {
"id": "iHBHe7ypzz4mZExM",
"name": "Notion account"
}
},
"retryOnFail": true,
"typeVersion": 4.2,
"waitBetweenTries": 3000
},
{
"id": "44c732a9-b805-432e-8e9c-ba279e4cca46",
"name": "Nota adhesiva5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
620
],
"parameters": {
"color": 7,
"width": 1340,
"height": 740,
"content": "## 8. Web Search and Extracting Web Page Contents using [APIFY.com](https://www.apify.com?fpr=414q6)\n[Read more about the HTTP Request node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest/)\n\nHere is where I deviated a little from the reference implementation. I opted not to use Firecrawl.ai due to (1) high cost of the service and (2) a regular non-ai crawler would work just as well and probably quicker. Instead I'm using [APIFY.com](https://www.apify.com?fpr=414q6) which is a more performant, cost-effective and reliable web scraper service. If you don't want to use Apify, feel free to swap this out with your preferred service.\n\nThis step is the most exciting in terms of improvements and optimisations eg. mix in internal data sources! Add in Perplexity.ai or Jina.ai! Possibilities are endless."
},
"typeVersion": 1
},
{
"id": "daf2e775-72d3-4366-882b-8c9eb65f11e8",
"name": "Nota adhesiva6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1140,
60
],
"parameters": {
"color": 7,
"width": 460,
"height": 360,
"content": "## 5. Set Report to In-Progress\n[Read more about the Notion node](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.notion/)"
},
"typeVersion": 1
},
{
"id": "2d1b394d-8b9a-43fc-a646-c4e05c92da5b",
"name": "Nota adhesiva7",
"type": "n8n-nodes-base.stickyNote",
"position": [
860,
780
],
"parameters": {
"color": 7,
"width": 800,
"height": 580,
"content": "## 9. Compile Learnings with Reasoning Model\n[Read more about the Basic LLM node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/)\n\nWith our gathered sources, it's now just a case of giving it to our LLM to compile a list of \"learnings\" from them. For our DeepResearcher, we'll use OpenAI's o3-mini which is the latest reasoning model at time of writing. Reasoning perform better than regular chat models due their chain-of-thought or \"thinking\" process that they perform.\n\nThe \"Learnings\" are then combined with the generated research goal to complete one loop."
},
"typeVersion": 1
},
{
"id": "e2c29aa2-ff79-4bdd-b3c7-cf5e5866db8a",
"name": "Obtener fila existente1",
"type": "n8n-nodes-base.notion",
"position": [
-1020,
1600
],
"parameters": {
"limit": 1,
"filters": {
"conditions": [
{
"key": "Request ID|rich_text",
"condition": "equals",
"richTextValue": "={{ $json.requestId.toString() }}"
}
]
},
"options": {},
"resource": "databasePage",
"matchType": "allFilters",
"operation": "getAll",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "19486dd6-0c0c-80da-9cb7-eb1468ea9afd",
"cachedResultUrl": "https://www.notion.so/19486dd60c0c80da9cb7eb1468ea9afd",
"cachedResultName": "n8n DeepResearch"
},
"filterType": "manual"
},
"credentials": {
"notionApi": {
"id": "iHBHe7ypzz4mZExM",
"name": "Notion account"
}
},
"typeVersion": 2.2
},
{
"id": "9dff368e-c282-4fef-8894-e218ea266695",
"name": "Nota adhesiva8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1140,
1400
],
"parameters": {
"color": 7,
"width": 660,
"height": 540,
"content": "## 10. Generate DeepSearch Report using Learnings\n[Read more about the Basic LLM node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm/)\n\nFinally! After all learnings have been gathered - which may have taken up to an hour or more on the higher settings! - they are given to our LLM to generate the final research report in markdown format. Technically, the DeepResearch ends here but for this template, we need to push the output to Notion. If you're not using Notion, feel free to ignore the last few steps."
},
"typeVersion": 1
},
{
"id": "14bfd0fd-6bc4-4dbf-86b2-44ef1c3586f7",
"name": "Nota adhesiva9",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
1400
],
"parameters": {
"color": 7,
"width": 1060,
"height": 540,
"content": "## 11. Reformat Report as Notion Blocks\n[Learn more about the Markdown node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.markdown/)\n\nTo write our report to our Notion page, we'll have to convert it to Notion \"blocks\" - these are specialised json objects which are required by the Notion API. There are quite a number of ways to do this conversion not involving the use of AI but for kicks, I decided to do so anyway. In this step, we first convert to HTML as it allows us to split the report semantically and makes for easier parsing for the LLM."
},
"typeVersion": 1
},
{
"id": "a2aff56d-78b9-40a4-ac78-bd8380802ea0",
"name": "Nota adhesiva10",
"type": "n8n-nodes-base.stickyNote",
"position": [
1220,
1400
],
"parameters": {
"color": 7,
"width": 800,
"height": 580,
"content": "## 13. Update Report in Notion\n[Read more about the HTTP request node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest/)\n\nIn this step, we can use the Notion API to add the blocks to our page sequentially. A loop is used due to the unstable Notion API - the loop allows retries for blocks that require it."
},
"typeVersion": 1
},
{
"id": "b5beeccd-e498-48ed-b6f2-b29d4599e2c9",
"name": "Nota adhesiva11",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1840,
-680
],
"parameters": {
"color": 7,
"width": 680,
"height": 560,
"content": "## 1. Let's Research!\n[Learn more about the form trigger node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.formtrigger)\n\nn8n forms are a really nice way to get our frontend up and running quickly and compared to chat, offers a superior user interface for user input. I've gone perhaps a little extra with the custom html fields but I do enjoy adding a little customisation now and then."
},
"typeVersion": 1
},
{
"id": "533ede84-1138-426c-93df-c2b862e2d063",
"name": "Informe de investigación profunda",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
-860,
1600
],
"parameters": {
"text": "=You are are an expert and insightful researcher.\n* Given the following prompt from the user, write a final report on the topic using the learnings from research.\n* Make it as as detailed as possible, aim for 3 or more pages, include ALL the learnings from research.\n* Format the report in markdown. Use headings, lists and tables only and where appropriate.\n\n<prompt>{{ $('JobType Router').first().json.data.query }}</prompt>\n\nHere are all the learnings from previous research:\n\n<learnings>\n{{\n$('JobType Router').first().json.data\n .all_learnings\n .map(item => `<learning>${item}</learning>`) \n .join('\\n')\n}}\n</learnings>",
"promptType": "define"
},
"typeVersion": 1.5
},
{
"id": "efe47725-7fd5-45e7-97c4-d6c133745e5f",
"name": "Hallazgos de investigación profunda",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
1120,
1020
],
"parameters": {
"text": "=Given the following contents from a SERP search for the query <query>{{ $('Item Ref').first().json.query }}</query>, generate a list of learnings from the contents. Return a maximum of 3 learnings, but feel free to return less if the contents are clear. Make sure each learning is unique and not similar to each other. The learnings should be concise and to the point, as detailed and infromation dense as possible. Make sure to include any entities like people, places, companies, products, things, etc in the learnings, as well as any exact metrics, numbers, or dates. The learnings will be used to research the topic further.\n\n<contents>\n{{\n$input\n .all()\n .map(item =>`<content>\\n${item.json.markdown.substr(0, 25_000)}\\n</content>`)\n .join('\\n')\n}}\n</contents>",
"messages": {
"messageValues": [
{
"type": "HumanMessagePromptTemplate",
"message": "=You are an expert researcher. Today is {{ $now.toLocaleString() }}. Follow these instructions when responding:\n - You may be asked to research subjects that is after your knowledge cutoff, assume the user is right when presented with news.\n - The user is a highly experienced analyst, no need to simplify it, be as detailed as possible and make sure your response is correct.\n - Be highly organized.\n - Suggest solutions that I didn't think about.\n - Be proactive and anticipate my needs.\n - Treat me as an expert in all subject matter.\n - Mistakes erode my trust, so be accurate and thorough.\n - Provide detailed explanations, I'm comfortable with lots of detail.\n - Value good arguments over authorities, the source is irrelevant.\n - Consider new technologies and contrarian ideas, not just the conventional wisdom.\n - You may use high levels of speculation or prediction, just flag it for me."
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"executeOnce": true,
"typeVersion": 1.5
},
{
"id": "d3b42d13-e8ca-4085-ace9-1d9fb53f5e71",
"name": "Generar informe",
"type": "n8n-nodes-base.executeWorkflow",
"position": [
480,
180
],
"parameters": {
"options": {
"waitForSubWorkflow": false
},
"workflowId": {
"__rl": true,
"mode": "id",
"value": "={{ $workflow.id }}"
},
"workflowInputs": {
"value": {
"data": "={{\n{\n ...Object.assign({}, $json),\n query: $('JobType Router').first().json.data.query\n}\n}}",
"jobType": "deepresearch_report",
"requestId": "={{ $('JobType Router').first().json.requestId }}"
},
"schema": [
{
"id": "requestId",
"display": true,
"required": false,
"displayName": "requestId",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "jobType",
"type": "string",
"display": true,
"required": false,
"displayName": "jobType",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "data",
"type": "object",
"display": true,
"required": false,
"displayName": "data",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": true
}
},
"typeVersion": 1.2
},
{
"id": "2b0314ff-cd82-4b3b-a4a9-5fd8067391eb",
"name": "Generar hallazgos",
"type": "n8n-nodes-base.executeWorkflow",
"position": [
-380,
180
],
"parameters": {
"mode": "each",
"options": {
"waitForSubWorkflow": true
},
"workflowId": {
"__rl": true,
"mode": "id",
"value": "={{ $workflow.id }}"
},
"workflowInputs": {
"value": {
"data": "={{ $json }}",
"jobType": "deepresearch_learnings",
"requestId": "={{ $('JobType Router').first().json.requestId }}"
},
"schema": [
{
"id": "requestId",
"display": true,
"removed": false,
"required": false,
"displayName": "requestId",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "jobType",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "jobType",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "data",
"type": "object",
"display": true,
"removed": false,
"required": false,
"displayName": "data",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": true
}
},
"typeVersion": 1.2
},
{
"id": "f4457d0b-d708-4bca-9973-46d96ed55826",
"name": "Confirmación",
"type": "n8n-nodes-base.form",
"position": [
780,
-420
],
"webhookId": "2eb17c47-c887-4e95-8641-1b3796452ab9",
"parameters": {
"options": {
"formTitle": "DeepResearcher",
"buttonLabel": "Done",
"formDescription": "=<img\n src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw\"\n width=\"100%\"\n style=\"border:1px solid #ccc\"\n/>\n<p style=\"text-align:left\">\n<strong style=\"display:block;font-family:sans-serif;font-weight:700;font-size:16px;margin-top:12px;margin-bottom:0;\">Your Report Is On Its Way!</strong>\n<br/>\nDeepResearcher will now work independently to conduct the research and the compiled report will be uploaded to the following Notion page below when finished.\n<br/><br/>\nPlease click the \"Done\" button to complete the form.\n</p>\n<hr style=\"display:block;margin-top:16px;margin-bottom:0\" />"
},
"formFields": {
"values": [
{
"html": "=<a href=\"{{ $json.url }}\" style=\"text-decoration:none\" target=\"_blank\">\n<div style=\"display:flex;text-align:left;font-family:sans-serif;\">\n <div style=\"width:150px;height:150px;padding:12px;\">\n <img src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/cajjymprexcoesu4gg9g\" width=\"100%\" />\n </div>\n <div style=\"width:100%;padding:12px;\">\n <div style=\"font-size:14px;font-weight:700\">{{ $json.name }}</div>\n <div style=\"font-size:12px;color:#666\">\n {{ $json.property_description }}\n </div>\n </div>\n</div>\n</a>",
"fieldType": "html"
}
]
}
},
"typeVersion": 1
},
{
"id": "af8fe17a-4314-4e92-ad8e-8be0be62984b",
"name": "Solicitud de investigación",
"type": "n8n-nodes-base.form",
"position": [
-1560,
-460
],
"webhookId": "46142c14-3692-40f6-80e5-f3d976e95191",
"parameters": {
"options": {
"formTitle": "DeepResearcher",
"formDescription": "=<img\n src=\"https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/o4wqztloz3j6okfxpeyw\"\n width=\"100%\"\n style=\"border:1px solid #ccc\"\n/>"
},
"formFields": {
"values": [
{
"fieldType": "textarea",
"fieldLabel": "What would you like to research?",
"requiredField": true
},
{
"html": "<video\n style=\"display:none\"\n src=\"/when_will_n8n_support_range_sliders.mp4\"\n onerror='\n this.insertAdjacentHTML(`afterend`,\n `<div class=\"form-group\" style=\"margin-bottom:16px;\">\n <label class=\"form-label\" for=\"breadth\">\n Enter research depth (Default 1)\n </label>\n <p style=\"font-size:12px;color:#666;text-align:left\">\n This value determines how many sub-queries to generate.\n </p>\n <input\n class=\"form-input\"\n type=\"range\"\n id=\"depth\"\n name=\"field-1\"\n value=\"1\"\n step=\"1\"\n max=\"5\"\n min=\"1\"\n list=\"depth-markers\"\n >\n <datalist\n id=\"depth-markers\"\n style=\"display: flex;\n flex-direction: row;\n justify-content: space-between;\n writing-mode: horizontal-tb;\n margin-top: -10px;\n text-align: center;\n font-size: 10px;\n margin-left: 16px;\n margin-right: 16px;\"\n >\n <option value=\"1\" label=\"1\"></option>\n <option value=\"2\" label=\"2\"></option>\n <option value=\"3\" label=\"3\"></option>\n <option value=\"4\" label=\"4\"></option>\n <option value=\"5\" label=\"5\"></option>\n </datalist>\n </div>`)\n '\n/>",
"fieldType": "html",
"elementName": "input-depth"
},
{
"html": "<video\n style=\"display:none\"\n src=\"/when_will_n8n_support_range_sliders.mp4\"\n onerror='\n this.insertAdjacentHTML(`afterend`,\n `<div class=\"form-group\" style=\"margin-bottom:16px;\">\n <label class=\"form-label\" for=\"breadth\">\n Enter research breadth (Default 2)\n </label>\n <p style=\"font-size:12px;color:#666;text-align:left\">\n This value determines how many sources to explore.\n </p>\n <input\n class=\"form-input\"\n type=\"range\"\n id=\"breadth\"\n name=\"field-2\"\n value=\"2\"\n step=\"1\"\n max=\"5\"\n min=\"1\"\n list=\"breadth-markers\"\n >\n <datalist\n id=\"breadth-markers\"\n style=\"display: flex;\n flex-direction: row;\n justify-content: space-between;\n writing-mode: horizontal-tb;\n margin-top: -10px;\n text-align: center;\n font-size: 10px;\n margin-left: 16px;\n margin-right: 16px;\"\n >\n <option value=\"1\" label=\"1\"></option>\n <option value=\"2\" label=\"2\"></option>\n <option value=\"3\" label=\"3\"></option>\n <option value=\"4\" label=\"4\"></option>\n <option value=\"5\" label=\"5\"></option>\n </datalist>\n </div>`)\n '\n/>\n",
"fieldType": "html",
"elementName": "input-breadth"
},
{
"fieldType": "dropdown",
"fieldLabel": "={{ \"\" }}",
"multiselect": true,
"fieldOptions": {
"values": [
{
"option": "=I understand higher depth and breath values I've selected may incur longer wait times and higher costs. I acknowledging this and wish to proceed with the research request."
}
]
},
"requiredField": true
}
]
}
},
"typeVersion": 1
},
{
"id": "c67a5e5c-f82b-4e8a-9c99-065d16dfa576",
"name": "Bloques válidos",
"type": "n8n-nodes-base.filter",
"position": [
740,
1600
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f68cefe0-e109-4d41-9aa3-043f3bc6c449",
"operator": {
"type": "string",
"operation": "notExists",
"singleValue": true
},
"leftValue": "={{ $json.error }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "b89cf700-d955-4de4-bbac-b5c55995a1ee",
"name": "Nota adhesiva12",
"type": "n8n-nodes-base.stickyNote",
"position": [
620,
1400
],
"parameters": {
"color": 7,
"width": 580,
"height": 580,
"content": "## 12. Append URL Sources List\n[Read more about the Code node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code)\n\nFor our source URLs, we'll manually compose the Notion blocks for them - this is because there's usually a lot of them! We'll then append to the end of the other blocks."
},
"typeVersion": 1
},
{
"id": "70c898a1-a757-452d-83ef-de1998fe13ae",
"name": "Anexar bloques",
"type": "n8n-nodes-base.merge",
"position": [
1000,
1760
],
"parameters": {},
"typeVersion": 3
},
{
"id": "591a3fcd-1748-43f7-9766-bc2059c195a0",
"name": "Fuentes URL a listas",
"type": "n8n-nodes-base.code",
"position": [
740,
1760
],
"parameters": {
"jsCode": "const urls = Object.values($('JobType Router').first().json.data.all_urls\n .reduce((acc, url) => ({ ...acc, [url]: url }),{}));\nconst chunksize = 50;\nconst splits = Math.max(1, Math.floor(urls.length/chunksize));\n\nconst blocks = Array(splits).fill(0)\n .map((_, idx) => {\n const block = urls\n .slice(\n idx * chunksize, \n (idx * chunksize) + chunksize - 1\n )\n .map(url => {\n return {\n object: \"block\",\n type: \"bulleted_list_item\",\n bulleted_list_item: {\n rich_text: [\n { type: \"text\", text: { content: url } }\n ]\n }\n }\n });\n return { json: { block } }\n });\n\nreturn [\n { json: {\n block:[{\n \"object\": \"block\",\n \"type\": \"heading_2\",\n \"heading_2\": {\n \"rich_text\": [\n {\n \"type\": \"text\",\n \"text\": {\n \"content\": \"Sources\"\n }\n }\n ]\n }\n }]\n } },\n ...blocks\n];"
},
"typeVersion": 2
},
{
"id": "e59dbeea-ccf3-4619-9fe1-24874a91bdab",
"name": "Respuesta vacía",
"type": "n8n-nodes-base.set",
"position": [
640,
1160
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1de40158-338b-4db3-9e22-6fd63b21f825",
"name": "ResearchGoal",
"type": "string",
"value": "={{ $('Item Ref').first().json.researchGoal }}"
},
{
"id": "9f59a2d4-5e5a-4d0b-8adf-2832ce746f0f",
"name": "learnings",
"type": "array",
"value": "={{ [] }}"
},
{
"id": "972ab5f5-0537-4755-afcb-d1db4f09ad60",
"name": "followUpQuestions",
"type": "array",
"value": "={{ [] }}"
},
{
"id": "90cef471-76b0-465d-91a4-a0e256335cd3",
"name": "urls",
"type": "array",
"value": "={{ [] }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "34035b2e-eee9-483e-8125-3b6f1f41cd1d",
"name": "¿Tiene contenido?",
"type": "n8n-nodes-base.if",
"position": [
480,
1020
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "1ef1039a-4792-47f9-860b-d2ffcffd7129",
"operator": {
"type": "object",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "5e9f80e2-db58-4f89-8aec-a1b8e73e18eb",
"name": "Nota adhesiva13",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1820,
-240
],
"parameters": {
"color": 5,
"width": 300,
"height": 100,
"content": "### Not using forms?\nFeel free ot swap this out for chat or even webhooks to fit your existing workflows."
},
"typeVersion": 1
},
{
"id": "3e513463-2f4c-4e3e-921d-e5c8ea5ec078",
"name": "Nota adhesiva14",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1880,
540
],
"parameters": {
"color": 5,
"width": 460,
"height": 240,
"content": "### 🚏 The Subworkflow Event Pattern \nIf you're new to n8n, this advanced technique might need some explaining but in gist, we're using subworkflows to run different parts of our DeepResearcher workflow as separate executions.\n\n* Necessary to implement the recursive loop mechanism needed to enable this workflow.\n* Negates the need to split this workflow into multiple templates.\n* Great generally for building high performance n8n workflows (a topic for a future post!)"
},
"typeVersion": 1
},
{
"id": "fea2568e-86c9-4663-b141-a9b2a36b84f5",
"name": "Nota adhesiva15",
"type": "n8n-nodes-base.stickyNote",
"position": [
720,
-60
],
"parameters": {
"color": 5,
"width": 340,
"height": 200,
"content": "### Recursive Looping\nThe recursive looping implemented for this workflow is an advanced item-linking technique. It works by specifically controlling which nodes \"execute once\" vs\" execute for each item\" because of this becareful of ermoving nodes! Always check the settings of the node you're replacing and ensure the settings match. "
},
"typeVersion": 1
},
{
"id": "fd3fec73-4b1a-4882-8c5a-d4825d9038ad",
"name": "Combinar y enviar al bucle",
"type": "n8n-nodes-base.aggregate",
"position": [
-220,
860
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "7c183897-e2ce-46da-90bd-0a39122b85f2",
"name": "Por cada bloque...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1440,
1600
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "bc04462a-780c-48e9-bc38-8eaf8ac1175c",
"name": "Nota adhesiva16",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2420,
-920
],
"parameters": {
"width": 520,
"height": 1060,
"content": "## n8n DeepResearcher\n### This template attempts to replicate OpenAI's DeepResearch feature which, at time of writing, is only available to their pro subscribers.\n\nThough the inner workings of DeepResearch have not been made public, it is presumed the feature relies on the ability to deep search the web, scrape web content and invoking reasoning models to generate reports. All of which n8n is really good at!\n\n### How it works\n* A form is used to first capture the user's research query and how deep they'd like the researcher to go.\n* Once submitted, a blank Notion page is created which will later hold the final report and the researcher gets to work.\n* The user's query goes through a recursive series of web serches and web scraping to collect data on the research topic to generate partial learnings.\n* Once complete, all learnings are combined and given to a reasoning LLM to generate the final report.\n* The report is then written to the placeholder Notion page created earlier. \n\n### How to use\n* Duplicate this Notion database to use with this template: https://jimleuk.notion.site/19486dd60c0c80da9cb7eb1468ea9afd?v=19486dd60c0c805c8e0c000ce8c87acf\n* Sign-up for [APIFY.com](https://www.apify.com?fpr=414q6) API Key for web search and scraping services.\n* Ensure you have access to OpenAI's o3-mini model. Alternatively, switch this out for o1 series.\n* You must publish this workflow and ensure the form url is publically accessible.\n\n### On Depth & Breadth Configuration\nFor more detailed reports, increase depth and breadth but be warned the workflow will take a exponentially more time and money to complete. The defaults are usually good enough.\n\nDepth=1 & Breadth=2 - will take about 5 - 10mins.\nDepth=1 & Breadth=3 - will take about 15 - 20mins.\nDpeth=3 & Breadth=5 - will take about 2+ hours!\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
},
"typeVersion": 1
},
{
"id": "654362c8-bc85-47d1-b277-50630f6f3999",
"name": "Nota adhesiva17",
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"index": 0
}
]
]
},
"a342005e-a88e-419b-b929-56ecbba4a936": {
"ai_outputParser": [
[
{
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"type": "ai_outputParser",
"index": 0
}
]
]
},
"703c57af-de19-4f00-b580-711a272fa5ca": {
"main": [
[
{
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"type": "main",
"index": 0
}
]
]
},
"525da936-a9eb-4523-b27a-ff6ae7b0e5ef": {
"ai_outputParser": [
[
{
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"type": "ai_outputParser",
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}
]
]
},
"34e1fa5d-bc0c-4b9e-84a7-35db2b08c772": {
"ai_outputParser": [
[
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},
"09a363f2-6300-430d-8c7e-3e1611ab8e68": {
"ai_outputParser": [
[
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}
}
}Preguntas frecuentes
¿Cómo usar este flujo de trabajo?
Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.
¿En qué escenarios es adecuado este flujo de trabajo?
Avanzado - Inteligencia Artificial
¿Es de pago?
Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.
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Información del flujo de trabajo
Nivel de dificultad
Avanzado
Número de nodos87
Categoría1
Tipos de nodos23
Descripción de la dificultad
Autor
Jimleuk
@jimleukFreelance consultant based in the UK specialising in AI-powered automations. I work with select clients tackling their most challenging projects. For business enquiries, send me an email at hello@jimle.uk LinkedIn: https://www.linkedin.com/in/jimleuk/ X/Twitter: https://x.com/jimle_uk
Enlaces externos
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